Artificial device and method of collecting image of the same

ABSTRACT

Provided are an intelligent device and a method of correcting an image thereof. The intelligent device includes a camera received in a body; a processor for controlling to obtain subject state information from an image transmitted from the camera; and a memory for storing the subject status information, wherein the processor is configured to determine a distortion state of a size of a subject based on the obtained subject state information, to measure a distance between the camera and the subject when the distortion state of the size of the subject is recognized, to correct a size of the subject to correspond to a ratio of the measured distance, and to correct a blank of a subject background formed by the corrected size of the subject. The intelligent device may be connected to an Artificial Intelligence (AI) module, a drone (Unmanned Aerial Vehicle (UAV)), a robot, an augmented reality (AR) device, a virtual reality (VR) device, and a device related to a 5G service.

This application claims the priority benefit of Korean PatentApplication No. 10-2019-0107803 filed on Aug. 30, 2019, which isincorporated herein by reference for all purposes as if fully set forthherein.

BACKGROUND OF THE INVENTION Field of the Invention

The present disclosure relates to an intelligent device and a method ofcorrecting an image thereof, and more particularly, to an intelligentdevice and a method of correcting an image thereof that can obtainvarious information related to a subject from a video or an imagephotographed by the intelligent device and that can learn based on theobtained various information to provide an optimal picture.

Related Art

An artificial intelligence (AI) system is a computer system thatimplements human-level intelligence, and unlike conventional rule-basedsmart systems, in the AI system, machines learn, determine, and becomesmart. The more artificial intelligence systems are used, the better arecognition rate and the more accurately a user's taste may beunderstood. Therefore, the conventional rule-based smart system isgradually being replaced with an artificial intelligence system based ondeep learning.

AI technology is configured with machine learning and element technologythat uses machine learning. Machine learning is algorithm technology forclassifying and learning feature information of data. Element technologyis technology that uses machine learning algorithm such as deeplearning, and may be configured with technical fields such as linguisticunderstanding, visual understanding, inference/prediction, knowledgeexpression, and motion control.

Recently, according to characteristics of a wide angle lens used indigital devices, there is a problem that a body ratio is not goodcompared to an original person when taking pictures of the person.

SUMMARY OF THE INVENTION

An object of the present disclosure is to address the above-describedand other needs and/or problems.

The present disclosure further provides an intelligent device and amethod of correcting an image thereof that can correct a size of arecognized subject based on a learned subject related informationaccording to a whole body photographing mode or a selfie photographingmode when photographing a subject using an intelligent device.

In an aspect, a method of correcting an image of an intelligent deviceincludes obtaining subject state information from an image; determininga distortion state of a size of the subject based on the obtainedsubject status information; measuring, when a distortion state of thesize of the subject is recognized, a distance between a camera and thesubject and correcting the size of the subject to correspond to a ratioof the measured distance; and correcting a blank of a subject backgroundformed by the corrected size of the subject.

The correcting of a blank may include obtaining background informationabout the subject through a preview image photographed beforephotographing.

The determining of a distortion state may include extracting featurevalues from the obtained subject state information; inputting thefeature values to an artificial neural network (ANN) classifier trainedto distinguish whether a size of the subject is in a distortion state;analyzing an output value of the ANN; and determining whether a size ofthe subject is in a distortion state based on the output value of theANN.

The method may further include determining, when a blank occurs in thebackground by a corrected size of the subject, whether there is abackground in the blank of the background; correcting, if there is abackground in the blank of the background, the background of the subjectusing the preview image; and correcting, if there is no background inthe blank, the blank using a surrounding background of the subject.

The method may further include increasing a correction range capable ofcorrecting a size of the subject as the measured distance is closer.

Gap correction using a surrounding background of the subject may includegenerating a background in the blank by combining the surroundingbackground of the subject and outlines of the subject.

The camera may include a time of flight (TOP) camera for shooting lightand measuring a time of reflected light to calculate a distance.

The method may further include receiving, from a network, downlinkcontrol information (DCI) used for scheduling transmission of thesubject state information obtained from at least one sensor providedinside the intelligent device, wherein the subject state information maybe transmitted to the network based on the DCI.

The method may further include performing an initial access procedurewith the network based on a synchronization signal block (SSB), whereinstatus information of the subject may be transmitted to the networkthrough a physical uplink shared channel (PUSCH), wherein DM-RSs of thePUSCH and the SSB may be QCL for QCL type D.

The method may further include controlling a communication unit totransmit status information of the subject to an AI processor includedin the network; and controlling the communication unit to receive AIprocessed information from the AI processor; and wherein the AIprocessed information may be information in which a size of the subjectis determined to any one of a normal state or a distorted state.

In another aspect, an intelligent device includes a camera received in abody; a processor for controlling to obtain subject state informationfrom an image transmitted from the camera; and a memory for storing thesubject status information, wherein the processor is configured todetermine a distortion state of a size of a subject based on theobtained subject state information, to measure a distance between thecamera and the subject when the distortion state of the size of thesubject is recognized, to correct the size of the subject to correspondto a ratio of the measured distance, and to correct a blank of a subjectbackground formed by the corrected size of the subject.

The processor may obtain background information about the subjectthrough a preview image photographed before photographing.

The processor may extract feature values from the obtained subject stateinformation, input the feature values to an artificial neural network(ANN) classifier trained to distinguish whether a size of the subject isin a distortion state, analyze an output value of the ANN, and determinewhether a size of the subject is in a distortion state based on theoutput value of the ANN.

The processor may determine, when a blank occurs in the background by acorrected size of the subject, whether there is a background in theblank of the background, correct a background of the subject using thepreview image, if there is a background in the blank of the background,and correct the blank using a surrounding background of the subject, ifthere is no background in the blank.

The processor may control to increase a correction range capable ofcorrecting a size of the subject as the measured distance is closer.

Gap correction using a surrounding background of the subject may includegenerating a background in the blank by combining the surroundingbackground of the subject and outlines of the subject.

The camera may include a time of flight (TOP) camera for shooting lightand measuring a time of reflected light to calculate a distance.

The processor may receive, from a network, downlink control information(DCI) used for scheduling transmission of the subject state informationobtained from at least one sensor provided inside the intelligentdevice, and wherein the subject state information may be transmitted tothe network based on the DCI.

The processor may perform an initial access procedure with the networkbased on a synchronization signal block (SSB), wherein statusinformation of the subject may be transmitted to the network through aphysical uplink shared channel (PUSCH), and wherein DM-RSs of the PUSCHand the SSB may be QCL for QCL type D.

The intelligent device may further include a communication unit, whereinthe processor may control to transmit state information of the subjectto an AI processor included in the network through the communicationunit, and control the communication unit to receive AI processedinformation from the AI processor, and wherein the AI processedinformation may be information in which a size of the subject isdetermined to any one of a normal state or a distorted state.

Effects of an intelligent device and a method of correcting an imagethereof according to an embodiment of the present disclosure will bedescribed as follows.

According to the present disclosure, when photographing a subject, asize of a recognized subject can be automatically corrected based onlearned subject related information according to a whole bodyphotographing mode or a selfie photographing mode.

According to the present disclosure, when photographing a subject, adistortion phenomenon of a recognized subject can be prevented accordingto a whole body photographing mode or a selfie photographing mode.

The effects of the present disclosure are not limited to theabove-described effects and the other effects will be understood bythose skilled in the art from the following description.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are included to provide a furtherunderstanding of the disclosure, illustrate embodiments of thedisclosure and together with the description serve to explain theprinciple of the disclosure.

FIG. 1 is a conceptual diagram illustrating one embodiment of an AIdevice.

FIG. 2 is a block diagram illustrating a wireless communication systemto which methods proposed in the present specification may be applied.

FIG. 3 is a diagram illustrating an example of a signaltransmitting/receiving method in a wireless communication system.

FIG. 4 illustrates an example of a basic operation of a user terminaland a 5G network in a 5G communication system.

FIG. 5 is a block diagram illustrating an intelligent device and an AIdevice related to the present disclosure.

FIGS. 6 and 7 are conceptual diagrams illustrating an example of anintelligent device related to the present disclosure viewed fromdifferent directions.

FIG. 8 is a block diagram illustrating an AI device according to anembodiment of the present disclosure.

FIG. 9 is a diagram illustrating an example of an artificial neuralnetwork model related to the present disclosure.

FIG. 10 is a flowchart illustrating a method of correcting an image ofan intelligent device according to one embodiment of the presentdisclosure.

FIG. 11 is a flowchart illustrating an example of determining adistortion state of a size of a subject in one embodiment of the presentdisclosure.

FIG. 12 is a flowchart illustrating another example of determining adistortion state of a size of a subject in one embodiment of the presentdisclosure.

FIG. 13 is a flowchart illustrating a method of correcting a backgroundof a subject according to one embodiment of the present disclosure.

FIG. 14 is a diagram illustrating an example of using a method ofcorrecting a background of a subject according to one embodiment of thepresent disclosure.

FIGS. 15 and 16 are diagrams illustrating a method of measuring adistance between an intelligent device and a subject according to oneembodiment of the present disclosure.

DESCRIPTION OF EXEMPLARY EMBODIMENTS

Hereinafter, embodiments of the disclosure will be described in detailwith reference to the attached drawings. The same or similar componentsare given the same reference numbers and redundant description thereofis omitted. The suffixes “module” and “unit” of elements herein are usedfor convenience of description and thus may be used interchangeably anddo not have any distinguishable meanings or functions. Further, in thefollowing description, if a detailed description of known techniquesassociated with the present disclosure would unnecessarily obscure thegist of the present disclosure, detailed description thereof will beomitted. In addition, the attached drawings are provided for easyunderstanding of embodiments of the disclosure and do not limittechnical spirits of the disclosure, and the embodiments should beconstrued as including all modifications, equivalents, and alternativesfalling within the spirit and scope of the embodiments.

While terms, such as “first”, “second”, etc., may be used to describevarious components, such components must not be limited by the aboveterms. The above terms are used only to distinguish one component fromanother.

When an element is “coupled” or “connected” to another element, itshould be understood that a third element may be present between the twoelements although the element may be directly coupled or connected tothe other element. When an element is “directly coupled” or “directlyconnected” to another element, it should be understood that no elementis present between the two elements.

The singular forms are intended to include the plural forms as well,unless the context clearly indicates otherwise.

In addition, in the specification, it will be further understood thatthe terms “comprise” and “include” specify the presence of statedfeatures, integers, steps, operations, elements, components, and/orcombinations thereof, but do not preclude the presence or addition ofone or more other features, integers, steps, operations, elements,components, and/or combinations.

[5G Scenario]

The three main requirement areas in the 5G system are (1) enhancedMobile Broadband (eMBB) area, (2) massive Machine Type Communication(mMTC) area, and (3) Ultra-Reliable and Low Latency Communication(URLLC) area.

Some use case may require a plurality of areas for optimization, butother use case may focus only one Key Performance Indicator (KPI). The5G system supports various use cases in a flexible and reliable manner.

eMBB far surpasses the basic mobile Internet access, supports variousinteractive works, and covers media and entertainment applications inthe cloud computing or augmented reality environment. Data is one ofcore driving elements of the 5G system, which is so abundant that forthe first time, the voice-only service may be disappeared. In the 5G,voice is expected to be handled simply by an application program using adata connection provided by the communication system. Primary causes ofincreased volume of traffic are increase of content size and increase ofthe number of applications requiring a high data transfer rate.Streaming service (audio and video), interactive video, and mobileInternet connection will be more heavily used as more and more devicesare connected to the Internet. These application programs requirealways-on connectivity to push real-time information and notificationsto the user. Cloud-based storage and applications are growing rapidly inthe mobile communication platforms, which may be applied to both ofbusiness and entertainment uses. And the cloud-based storage is aspecial use case that drives growth of uplink data transfer rate. The 5Gis also used for cloud-based remote works and requires a much shorterend-to-end latency to ensure excellent user experience when a tactileinterface is used. Entertainment, for example, cloud-based game andvideo streaming, is another core element that strengthens therequirement for mobile broadband capability. Entertainment is essentialfor smartphones and tablets in any place including a high mobilityenvironment such as a train, car, and plane. Another use case isaugmented reality for entertainment and information search. Here,augmented reality requires very low latency and instantaneous datatransfer.

Also, one of highly expected 5G use cases is the function that connectsembedded sensors seamlessly in every possible area, namely the use casebased on mMTC. Up to 2020, the number of potential IoT devices isexpected to reach 20.4 billion. Industrial IoT is one of key areas wherethe 5G performs a primary role to maintain infrastructure for smartcity, asset tracking, smart utility, agriculture and security.

URLLC includes new services which may transform industry throughultra-reliable/ultra-low latency links, such as remote control of majorinfrastructure and self-driving cars. The level of reliability andlatency are essential for smart grid control, industry automation,robotics, and drone control and coordination.

Next, a plurality of use cases will be described in more detail.

The 5G may complement Fiber-To-The-Home (FTTH) and cable-based broadband(or DOCSIS) as a means to provide a stream estimated to occupy hundredsof megabits per second up to gigabits per second. This fast speed isrequired not only for virtual reality and augmented reality but also fortransferring video with a resolution more than 4K (6K, 8K or more). VRand AR applications almost always include immersive sports games.Specific application programs may require a special networkconfiguration. For example, in the case of VR game, to minimize latency,game service providers may have to integrate a core server with the edgenetwork service of the network operator.

Automobiles are expected to be a new important driving force for the 5Gsystem together with various use cases of mobile communication forvehicles. For example, entertainment for passengers requires highcapacity and high mobile broadband at the same time. This is so becauseusers continue to expect a high-quality connection irrespective of theirlocation and moving speed. Another use case in the automotive field isan augmented reality dashboard. The augmented reality dashboard overlaysinformation, which is a perception result of an object in the dark andcontains distance to the object and object motion, on what is seenthrough the front window. In a future, a wireless module enablescommunication among vehicles, information exchange between a vehicle andsupporting infrastructure, and information exchange among a vehicle andother connected devices (for example, devices carried by a pedestrian).A safety system guides alternative courses of driving so that a drivermay drive his or her vehicle more safely and to reduce the risk ofaccident. The next step will be a remotely driven or self-drivenvehicle. This step requires highly reliable and highly fastcommunication between different self-driving vehicles and between aself-driving vehicle and infrastructure. In the future, it is expectedthat a self-driving vehicle takes care of all of the driving activitieswhile a human driver focuses on dealing with an abnormal drivingsituation that the self-driving vehicle is unable to recognize.Technical requirements of a self-driving vehicle demand ultra-lowlatency and ultra-fast reliability up to the level that traffic safetymay not be reached by human drivers.

The smart city and smart home, which are regarded as essential torealize a smart society, will be embedded into a high-density wirelesssensor network. Distributed networks comprising intelligent sensors mayidentify conditions for cost-efficient and energy-efficient conditionsfor maintaining cities and homes. A similar configuration may be appliedfor each home. Temperature sensors, window and heating controllers,anti-theft alarm devices, and home appliances will be all connectedwirelessly. Many of these sensors typified with a low data transferrate, low power, and low cost. However, for example, real-time HD videomay require specific types of devices for the purpose of surveillance.

As consumption and distribution of energy including heat or gas is beinghighly distributed, automated control of a distributed sensor network isrequired. A smart grid collects information and interconnect sensors byusing digital information and communication technologies so that thedistributed sensor network operates according to the collectedinformation. Since the information may include behaviors of energysuppliers and consumers, the smart grid may help improving distributionof fuels such as electricity in terms of efficiency, reliability,economics, production sustainability, and automation. The smart grid maybe regarded as a different type of sensor network with a low latency.

The health-care sector has many application programs that may benefitfrom mobile communication. A communication system may supporttelemedicine providing a clinical care from a distance. Telemedicine mayhelp reduce barriers to distance and improve access to medical servicesthat are not readily available in remote rural areas. It may also beused to save lives in critical medical and emergency situations. Awireless sensor network based on mobile communication may provide remotemonitoring and sensors for parameters such as the heart rate and bloodpressure.

Wireless and mobile communication are becoming increasingly importantfor industrial applications. Cable wiring requires high installation andmaintenance costs. Therefore, replacement of cables with reconfigurablewireless links is an attractive opportunity for many industrialapplications. However, to exploit the opportunity, the wirelessconnection is required to function with a latency similar to that in thecable connection, to be reliable and of large capacity, and to bemanaged in a simple manner. Low latency and very low error probabilityare new requirements that lead to the introduction of the 5G system.

Logistics and freight tracking are important use cases of mobilecommunication, which require tracking of an inventory and packages fromany place by using location-based information system. The use oflogistics and freight tracking typically requires a low data rate butrequires large-scale and reliable location information.

The present disclosure to be described below may be implemented bycombining or modifying the respective embodiments to satisfy theaforementioned requirements of the 5G system.

FIG. 1 illustrates one embodiment of an AI device.

Referring to FIG. 1, in the AI system, at least one or more of an AIserver 16, robot 11, self-driving vehicle 12, XR device 13, smartphone14, or home appliance 15 are connected to a cloud network 10. Here, therobot 11, self-driving vehicle 12, XR device 13, smartphone 14, or homeappliance 15 to which the AI technology has been applied may be referredto as an AI device (11 to 15).

The cloud network 10 may comprise part of the cloud computinginfrastructure or refer to a network existing in the cloud computinginfrastructure. Here, the cloud network 10 may be constructed by usingthe 3G network, 4G or Long Term Evolution (LTE) network, or 5G network.

In other words, individual devices (11 to 16) constituting the AI systemmay be connected to each other through the cloud network 10. Inparticular, each individual device (11 to 16) may communicate with eachother through the eNB but may communicate directly to each other withoutrelying on the eNB.

The AI server 16 may include a server performing AI processing and aserver performing computations on big data.

The AI server 16 may be connected to at least one or more of the robot11, self-driving vehicle 12, XR device 13, smartphone 14, or homeappliance 15, which are AI devices constituting the AI system, throughthe cloud network 10 and may help at least part of AI processingconducted in the connected AI devices (11 to 15).

At this time, the AI server 16 may teach the artificial neural networkaccording to a machine learning algorithm on behalf of the AI device (11to 15), directly store the learning model, or transmit the learningmodel to the AI device (11 to 15).

At this time, the AI server 16 may receive input data from the AI device(11 to 15), infer a result value from the received input data by usingthe learning model, generate a response or control command based on theinferred result value, and transmit the generated response or controlcommand to the AI device (11 to 15).

Similarly, the AI device (11 to 15) may infer a result value from theinput data by employing the learning model directly and generate aresponse or control command based on the inferred result value.

<AI+Robot>

By employing the AI technology, the robot 11 may be implemented as aguide robot, transport robot, cleaning robot, wearable robot,entertainment robot, pet robot, or unmanned flying robot.

The robot 11 may include a robot control module for controlling itsmotion, where the robot control module may correspond to a softwaremodule or a chip which implements the software module in the form of ahardware device.

The robot 11 may obtain status information of the robot 11, detect(recognize) the surroundings and objects, generate map data, determine atravel path and navigation plan, determine a response to userinteraction, or determine motion by using sensor information obtainedfrom various types of sensors.

Here, the robot 11 may use sensor information obtained from at least oneor more sensors among lidar, radar, and camera to determine a travelpath and navigation plan.

The robot 11 may perform the operations above by using a learning modelbuilt on at least one or more artificial neural networks. For example,the robot 11 may recognize the surroundings and objects by using thelearning model and determine its motion by using the recognizedsurroundings or object information. Here, the learning model may be theone trained by the robot 11 itself or trained by an external device suchas the AI server 16.

At this time, the robot 11 may perform the operation by generating aresult by employing the learning model directly but also perform theoperation by transmitting sensor information to an external device suchas the AI server 16 and receiving a result generated accordingly.

The robot 11 may determine a travel path and navigation plan by using atleast one or more of object information detected from the map data andsensor information or object information obtained from an externaldevice and navigate according to the determined travel path andnavigation plan by controlling its locomotion platform.

Map data may include object identification information about variousobjects disposed in the space in which the robot 11 navigates. Forexample, the map data may include object identification informationabout static objects such as wall and doors and movable objects such asa flowerpot and a desk. And the object identification information mayinclude the name, type, distance, location, and so on.

Also, the robot 11 may perform the operation or navigate the space bycontrolling its locomotion platform based on the control/interaction ofthe user. At this time, the robot 11 may obtain intention information ofthe interaction due to the user's motion or voice command and perform anoperation by determining a response based on the obtained intentioninformation.

<AI+Autonomous Navigation>

By employing the AI technology, the self-driving vehicle 12 may beimplemented as a mobile robot, unmanned ground vehicle, or unmannedaerial vehicle.

The self-driving vehicle 12 may include an autonomous navigation modulefor controlling its autonomous navigation function, where the autonomousnavigation control module may correspond to a software module or a chipwhich implements the software module in the form of a hardware device.The autonomous navigation control module may be installed inside theself-driving vehicle 12 as a constituting element thereof or may beinstalled outside the self-driving vehicle 12 as a separate hardwarecomponent.

The self-driving vehicle 12 may obtain status information of theself-driving vehicle 12, detect (recognize) the surroundings andobjects, generate map data, determine a travel path and navigation plan,or determine motion by using sensor information obtained from varioustypes of sensors.

Like the robot 11, the self-driving vehicle 12 may use sensorinformation obtained from at least one or more sensors among lidar,radar, and camera to determine a travel path and navigation plan.

In particular, the self-driving vehicle 12 may recognize an occludedarea or an area extending over a predetermined distance or objectslocated across the area by collecting sensor information from externaldevices or receive recognized information directly from the externaldevices.

The self-driving vehicle 12 may perform the operations above by using alearning model built on at least one or more artificial neural networks.For example, the self-driving vehicle 12 may recognize the surroundingsand objects by using the learning model and determine its navigationroute by using the recognized surroundings or object information. Here,the learning model may be the one trained by the self-driving vehicle 12itself or trained by an external device such as the AI server 16.

At this time, the self-driving vehicle 12 may perform the operation bygenerating a result by employing the learning model directly but alsoperform the operation by transmitting sensor information to an externaldevice such as the AI server 16 and receiving a result generatedaccordingly.

The self-driving vehicle 12 may determine a travel path and navigationplan by using at least one or more of object information detected fromthe map data and sensor information or object information obtained froman external device and navigate according to the determined travel pathand navigation plan by controlling its driving platform.

Map data may include object identification information about variousobjects disposed in the space (for example, road) in which theself-driving vehicle 12 navigates. For example, the map data may includeobject identification information about static objects such asstreetlights, rocks and buildings and movable objects such as vehiclesand pedestrians. And the object identification information may includethe name, type, distance, location, and so on.

Also, the self-driving vehicle 12 may perform the operation or navigatethe space by controlling its driving platform based on thecontrol/interaction of the user. At this time, the self-driving vehicle12 may obtain intention information of the interaction due to the user'smotion or voice command and perform an operation by determining aresponse based on the obtained intention information.

<AI+XR>

By employing the AI technology, the XR device 13 may be implemented as aHead-Mounted Display (HMD), Head-Up Display (HUD) installed at thevehicle, TV, mobile phone, smartphone, computer, wearable device, homeappliance, digital signage, vehicle, robot with a fixed platform, ormobile robot.

The XR device 13 may obtain information about the surroundings orphysical objects by generating position and attribute data about 3Dpoints by analyzing 3D point cloud or image data acquired from varioussensors or external devices and output objects in the form of XR objectsby rendering the objects for display.

The XR device 13 may perform the operations above by using a learningmodel built on at least one or more artificial neural networks. Forexample, the XR device 13 may recognize physical objects from 3D pointcloud or image data by using the learning model and provide informationcorresponding to the recognized physical objects. Here, the learningmodel may be the one trained by the XR device 13 itself or trained by anexternal device such as the AI server 16.

At this time, the XR device 13 may perform the operation by generating aresult by employing the learning model directly but also perform theoperation by transmitting sensor information to an external device suchas the AI server 16 and receiving a result generated accordingly.

<AI+Robot+Autonomous Navigation>

By employing the AI and autonomous navigation technologies, the robot 11may be implemented as a guide robot, transport robot, cleaning robot,wearable robot, entertainment robot, pet robot, or unmanned flyingrobot.

The robot 11 employing the AI and autonomous navigation technologies maycorrespond to a robot itself having an autonomous navigation function ora robot 11 interacting with the self-driving vehicle 12.

The robot 11 having the autonomous navigation function may correspondcollectively to the devices which may move autonomously along a givenpath without control of the user or which may move by determining itspath autonomously.

The robot 11 and the self-driving vehicle 12 having the autonomousnavigation function may use a common sensing method to determine one ormore of the travel path or navigation plan. For example, the robot 11and the self-driving vehicle 12 having the autonomous navigationfunction may determine one or more of the travel path or navigation planby using the information sensed through lidar, radar, and camera.

The robot 11 interacting with the self-driving vehicle 12, which existsseparately from the self-driving vehicle 12, may be associated with theautonomous navigation function inside or outside the self-drivingvehicle 12 or perform an operation associated with the user riding theself-driving vehicle 12.

At this time, the robot 11 interacting with the self-driving vehicle 12may obtain sensor information in place of the self-driving vehicle 12and provide the sensed information to the self-driving vehicle 12; ormay control or assist the autonomous navigation function of theself-driving vehicle 12 by obtaining sensor information, generatinginformation of the surroundings or object information, and providing thegenerated information to the self-driving vehicle 12.

Also, the robot 11 interacting with the self-driving vehicle 12 maycontrol the function of the self-driving vehicle 12 by monitoring theuser riding the self-driving vehicle 12 or through interaction with theuser. For example, if it is determined that the driver is drowsy, therobot 11 may activate the autonomous navigation function of theself-driving vehicle 12 or assist the control of the driving platform ofthe self-driving vehicle 12. Here, the function of the self-drivingvehicle 12 controlled by the robot 12 may include not only theautonomous navigation function but also the navigation system installedinside the self-driving vehicle 12 or the function provided by the audiosystem of the self-driving vehicle 12.

Also, the robot 11 interacting with the self-driving vehicle 12 mayprovide information to the self-driving vehicle 12 or assist functionsof the self-driving vehicle 12 from the outside of the self-drivingvehicle 12. For example, the robot 11 may provide traffic informationincluding traffic sign information to the self-driving vehicle 12 like asmart traffic light or may automatically connect an electric charger tothe charging port by interacting with the self-driving vehicle 12 likean automatic electric charger of the electric vehicle.

<AI+Robot+XR>

By employing the AI technology, the robot 11 may be implemented as aguide robot, transport robot, cleaning robot, wearable robot,entertainment robot, pet robot, or unmanned flying robot.

The robot 11 employing the XR technology may correspond to a robot whichacts as a control/interaction target in the XR image. In this case, therobot 11 may be distinguished from the XR device 13, both of which mayoperate in conjunction with each other.

If the robot 11, which acts as a control/interaction target in the XRimage, obtains sensor information from the sensors including a camera,the robot 11 or XR device 13 may generate an XR image based on thesensor information, and the XR device 13 may output the generated XRimage. And the robot 11 may operate based on the control signal receivedthrough the XR device 13 or based on the interaction with the user.

For example, the user may check the XR image corresponding to theviewpoint of the robot 11 associated remotely through an external devicesuch as the XR device 13, modify the navigation path of the robot 11through interaction, control the operation or navigation of the robot11, or check the information of nearby objects.

<AI+Autonomous Navigation+XR>

By employing the AI and XR technologies, the self-driving vehicle 12 maybe implemented as a mobile robot, unmanned ground vehicle, or unmannedaerial vehicle.

The self-driving vehicle 12 employing the XR technology may correspondto a self-driving vehicle having a means for providing XR images or aself-driving vehicle which acts as a control/interaction target in theXR image. In particular, the self-driving vehicle 12 which acts as acontrol/interaction target in the XR image may be distinguished from theXR device 13, both of which may operate in conjunction with each other.

The self-driving vehicle 12 having a means for providing XR images mayobtain sensor information from sensors including a camera and output XRimages generated based on the sensor information obtained. For example,by displaying an XR image through HUD, the self-driving vehicle 12 mayprovide XR images corresponding to physical objects or image objects tothe passenger.

At this time, if an XR object is output on the HUD, at least part of theXR object may be output so as to be overlapped with the physical objectat which the passenger gazes. On the other hand, if an XR object isoutput on a display installed inside the self-driving vehicle 12, atleast part of the XR object may be output so as to be overlapped with animage object. For example, the self-driving vehicle 12 may output XRobjects corresponding to the objects such as roads, other vehicles,traffic lights, traffic signs, bicycles, pedestrians, and buildings.

If the self-driving vehicle 12, which acts as a control/interactiontarget in the XR image, obtains sensor information from the sensorsincluding a camera, the self-driving vehicle 12 or XR device 13 maygenerate an XR image based on the sensor information, and the XR device13 may output the generated XR image. And the self-driving vehicle 12may operate based on the control signal received through an externaldevice such as the XR device 13 or based on the interaction with theuser.

[Extended Reality Technology]

eXtended Reality (XR) refers to all of Virtual Reality (VR), AugmentedReality (AR), and Mixed Reality (MR). The VR technology provides objectsor backgrounds of the real world only in the form of CG images, ARtechnology provides virtual CG images overlaid on the physical objectimages, and MR technology employs computer graphics technology to mixand merge virtual objects with the real world.

MR technology is similar to AR technology in a sense that physicalobjects are displayed together with virtual objects. However, whilevirtual objects supplement physical objects in the AR, virtual andphysical objects co-exist as equivalents in the MR.

The XR technology may be applied to Head-Mounted Display (HMD), Head-UpDisplay (HUD), mobile phone, tablet PC, laptop computer, desktopcomputer, TV, digital signage, and so on, where a device employing theXR technology may be called an XR device.

Hereinafter, 5G communication (5th generation mobile communication)required by an apparatus requiring AI processed information and/or an AIprocessor will be described through paragraphs A through G.

A. Example of Block Diagram of UE and 5G Network

FIG. 2 is a block diagram of a wireless communication system to whichmethods proposed in the disclosure are applicable.

Referring to FIG. 2, a device (autonomous device) including anautonomous module is defined as a first communication device (910 ofFIG. 2), and a processor 911 can perform detailed autonomous operations.

A 5G network including another vehicle communicating with the autonomousdevice is defined as a second communication device (920 of FIG. 2), anda processor 921 can perform detailed autonomous operations.

The 5G network may be represented as the first communication device andthe autonomous device may be represented as the second communicationdevice.

For example, the first communication device or the second communicationdevice may be a base station, a network node, a transmission terminal, areception terminal, a wireless device, a wireless communication device,an autonomous device, or the like.

For example, the first communication device or the second communicationdevice may be a base station, a network node, a transmission terminal, areception terminal, a wireless device, a wireless communication device,a vehicle, a vehicle having an autonomous function, a connected car, adrone (Unmanned Aerial Vehicle, UAV), and AI (Artificial Intelligence)module, a robot, an AR (Augmented Reality) device, a VR (VirtualReality) device, an MR (Mixed Reality) device, a hologram device, apublic safety device, an MTC device, an IoT device, a medical device, aFin Tech device (or financial device), a security device, aclimate/environment device, a device associated with 5G services, orother devices associated with the fourth industrial revolution field.

For example, a terminal or user equipment (UE) may include a cellularphone, a smart phone, a laptop computer, a digital broadcast terminal,personal digital assistants (PDAs), a portable multimedia player (PMP),a navigation device, a slate PC, a tablet PC, an ultrabook, a wearabledevice (e.g., a smartwatch, a smart glass and a head mounted display(HMD)), etc. For example, the HMD may be a display device worn on thehead of a user. For example, the HMD may be used to realize VR, AR orMR. For example, the drone may be a flying object that flies by wirelesscontrol signals without a person therein. For example, the VR device mayinclude a device that implements objects or backgrounds of a virtualworld. For example, the AR device may include a device that connects andimplements objects or background of a virtual world to objects,backgrounds, or the like of a real world. For example, the MR device mayinclude a device that unites and implements objects or background of avirtual world to objects, backgrounds, or the like of a real world. Forexample, the hologram device may include a device that implements360-degree 3D images by recording and playing 3D information using theinterference phenomenon of light that is generated by two lasers meetingeach other which is called holography. For example, the public safetydevice may include an image repeater or an imaging device that can beworn on the body of a user. For example, the MTC device and the IoTdevice may be devices that do not require direct interference oroperation by a person. For example, the MTC device and the IoT devicemay include a smart meter, a bending machine, a thermometer, a smartbulb, a door lock, various sensors, or the like. For example, themedical device may be a device that is used to diagnose, treat,attenuate, remove, or prevent diseases. For example, the medical devicemay be a device that is used to diagnose, treat, attenuate, or correctinjuries or disorders. For example, the medial device may be a devicethat is used to examine, replace, or change structures or functions. Forexample, the medical device may be a device that is used to controlpregnancy. For example, the medical device may include a device formedical treatment, a device for operations, a device for (external)diagnose, a hearing aid, an operation device, or the like. For example,the security device may be a device that is installed to prevent adanger that is likely to occur and to keep safety. For example, thesecurity device may be a camera, a CCTV, a recorder, a black box, or thelike. For example, the Fin Tech device may be a device that can providefinancial services such as mobile payment.

Referring to FIG. 2, the first communication device 910 and the secondcommunication device 920 include processors 911 and 921, memories 914and 924, one or more Tx/Rx radio frequency (RF) modules 915 and 925, Txprocessors 912 and 922, Rx processors 913 and 923, and antennas 916 and926. The Tx/Rx module is also referred to as a transceiver. Each Tx/Rxmodule 915 transmits a signal through each antenna 926. The processorimplements the aforementioned functions, processes and/or methods. Theprocessor 921 may be related to the memory 924 that stores program codeand data. The memory may be referred to as a computer-readable medium.More specifically, the Tx processor 912 implements various signalprocessing functions with respect to L1 (i.e., physical layer) in DL(communication from the first communication device to the secondcommunication device). The Rx processor implements various signalprocessing functions of L1 (i.e., physical layer).

UL (communication from the second communication device to the firstcommunication device) is processed in the first communication device 910in a way similar to that described in association with a receiverfunction in the second communication device 920. Each Tx/Rx module 925receives a signal through each antenna 926. Each Tx/Rx module providesRF carriers and information to the Rx processor 923. The processor 921may be related to the memory 924 that stores program code and data. Thememory may be referred to as a computer-readable medium.

B. Signal Transmission/Reception Method in Wireless Communication System

FIG. 3 is a diagram showing an example of a signaltransmission/reception method in a wireless communication system.

Referring to FIG. 3, when a UE is powered on or enters a new cell, theUE performs an initial cell search operation such as synchronizationwith a BS (S201). For this operation, the UE can receive a primarysynchronization channel (P-SCH) and a secondary synchronization channel(S-SCH) from the BS to synchronize with the BS and acquire informationsuch as a cell ID. In LTE and NR systems, the P-SCH and S-SCH arerespectively called a primary synchronization signal (PSS) and asecondary synchronization signal (SSS). After initial cell search, theUE can acquire broadcast information in the cell by receiving a physicalbroadcast channel (PBCH) from the BS. Further, the UE can receive adownlink reference signal (DL RS) in the initial cell search step tocheck a downlink channel state. After initial cell search, the UE canacquire more detailed system information by receiving a physicaldownlink shared channel (PDSCH) according to a physical downlink controlchannel (PDCCH) and information included in the PDCCH (S202).

Meanwhile, when the UE initially accesses the BS or has no radioresource for signal transmission, the UE can perform a random accessprocedure (RACH) for the BS (steps S203 to S206). To this end, the UEcan transmit a specific sequence as a preamble through a physical randomaccess channel (PRACH) (S203 and S205) and receive a random accessresponse (RAR) message for the preamble through a PDCCH and acorresponding PDSCH (S204 and S206). In the case of a contention-basedRACH, a contention resolution procedure may be additionally performed.

After the UE performs the above-described process, the UE can performPDCCH/PDSCH reception (S207) and physical uplink shared channel(PUSCH)/physical uplink control channel (PUCCH) transmission (S208) asnormal uplink/downlink signal transmission processes. Particularly, theUE receives downlink control information (DCI) through the PDCCH. The UEmonitors a set of PDCCH candidates in monitoring occasions set for oneor more control element sets (CORESET) on a serving cell according tocorresponding search space configurations. A set of PDCCH candidates tobe monitored by the UE is defined in terms of search space sets, and asearch space set may be a common search space set or a UE-specificsearch space set. CORESET includes a set of (physical) resource blockshaving a duration of one to three OFDM symbols. A network can configurethe UE such that the UE has a plurality of CORESETs. The UE monitorsPDCCH candidates in one or more search space sets. Here, monitoringmeans attempting decoding of PDCCH candidate(s) in a search space. Whenthe UE has successfully decoded one of PDCCH candidates in a searchspace, the UE determines that a PDCCH has been detected from the PDCCHcandidate and performs PDSCH reception or PUSCH transmission on thebasis of DCI in the detected PDCCH. The PDCCH can be used to schedule DLtransmissions over a PDSCH and UL transmissions over a PUSCH. Here, theDCI in the PDCCH includes downlink assignment (i.e., downlink grant (DLgrant)) related to a physical downlink shared channel and including atleast a modulation and coding format and resource allocationinformation, or an uplink grant (UL grant) related to a physical uplinkshared channel and including a modulation and coding format and resourceallocation information.

An initial access (IA) procedure in a 5G communication system will beadditionally described with reference to FIG. 3.

The UE can perform cell search, system information acquisition, beamalignment for initial access, and DL measurement on the basis of an SSB.The SSB is interchangeably used with a synchronization signal/physicalbroadcast channel (SS/PBCH) block.

The SSB includes a PSS, an SSS and a PBCH. The SSB is configured in fourconsecutive OFDM symbols, and a PSS, a PBCH, an SSS/PBCH or a PBCH istransmitted for each OFDM symbol. Each of the PSS and the SSS includesone OFDM symbol and 127 subcarriers, and the PBCH includes 3 OFDMsymbols and 576 subcarriers.

Cell search refers to a process in which a UE acquires time/frequencysynchronization of a cell and detects a cell identifier (ID) (e.g.,physical layer cell ID (PCI)) of the cell. The PSS is used to detect acell ID in a cell ID group and the SSS is used to detect a cell IDgroup. The PBCH is used to detect an SSB (time) index and a half-frame.

There are 336 cell ID groups and there are 3 cell IDs per cell ID group.A total of 1008 cell IDs are present. Information on a cell ID group towhich a cell ID of a cell belongs is provided/acquired through an SSS ofthe cell, and information on the cell ID among 336 cell ID groups isprovided/acquired through a PSS.

The SSB is periodically transmitted in accordance with SSB periodicity.A default SSB periodicity assumed by a UE during initial cell search isdefined as 20 ms. After cell access, the SSB periodicity can be set toone of {5 ms, 10 ms, 20 ms, 40 ms, 80 ms, 160 ms} by a network (e.g., aBS).

Next, acquisition of system information (SI) will be described.

SI is divided into a master information block (MIB) and a plurality ofsystem information blocks (SIBs). SI other than the MIB may be referredto as remaining minimum system information. The MIB includesinformation/parameter for monitoring a PDCCH that schedules a PDSCHcarrying SIB1 (SystemInformationBlock1) and is transmitted by a BSthrough a PBCH of an SSB. SIB1 includes information related toavailability and scheduling (e.g., transmission periodicity andSI-window size) of the remaining SIBs (hereinafter, SIBx, x is aninteger equal to or greater than 2). SiBx is included in an SI messageand transmitted over a PDSCH. Each SI message is transmitted within aperiodically generated time window (i.e., SI-window).

A random access (RA) procedure in a 5G communication system will beadditionally described with reference to FIG. 3.

A random access procedure is used for various purposes. For example, therandom access procedure can be used for network initial access,handover, and UE-triggered UL data transmission. A UE can acquire ULsynchronization and UL transmission resources through the random accessprocedure. The random access procedure is classified into acontention-based random access procedure and a contention-free randomaccess procedure. A detailed procedure for the contention-based randomaccess procedure is as follows.

A UE can transmit a random access preamble through a PRACH as Msg1 of arandom access procedure in UL. Random access preamble sequences havingdifferent two lengths are supported. A long sequence length 839 isapplied to subcarrier spacings of 1.25 kHz and 5 kHz and a shortsequence length 139 is applied to subcarrier spacings of 15 kHz, 30 kHz,60 kHz and 120 kHz.

When a BS receives the random access preamble from the UE, the BStransmits a random access response (RAR) message (Msg2) to the UE. APDCCH that schedules a PDSCH carrying a RAR is CRC masked by a randomaccess (RA) radio network temporary identifier (RNTI) (RA-RNTI) andtransmitted. Upon detection of the PDCCH masked by the RA-RNTI, the UEcan receive a RAR from the PDSCH scheduled by DCI carried by the PDCCH.The UE checks whether the RAR includes random access responseinformation with respect to the preamble transmitted by the UE, that is,Msg1. Presence or absence of random access information with respect toMsg1 transmitted by the UE can be determined according to presence orabsence of a random access preamble ID with respect to the preambletransmitted by the UE. If there is no response to Msg1, the UE canretransmit the RACH preamble less than a predetermined number of timeswhile performing power ramping. The UE calculates PRACH transmissionpower for preamble retransmission on the basis of most recent pathlossand a power ramping counter.

The UE can perform UL transmission through Msg3 of the random accessprocedure over a physical uplink shared channel on the basis of therandom access response information. Msg3 can include an RRC connectionrequest and a UE ID. The network can transmit Msg4 as a response toMsg3, and Msg4 can be handled as a contention resolution message on DL.The UE can enter an RRC connected state by receiving Msg4.

C. Beam Management (BM) Procedure of 5G Communication System

A BM procedure can be divided into (1) a DL MB procedure using an SSB ora CSI-RS and (2) a UL BM procedure using a sounding reference signal(SRS). In addition, each BM procedure can include Tx beam swiping fordetermining a Tx beam and Rx beam swiping for determining an Rx beam.

The DL BM procedure using an SSB will be described.

Configuration of a beam report using an SSB is performed when channelstate information (CSI)/beam is configured in RRC_CONNECTED.

-   -   A UE receives a CSI-ResourceConfig IE including        CSI-SSB-ResourceSetList for SSB resources used for BM from a BS.        The RRC parameter “csi-SSB-ResourceSetList” represents a list of        SSB resources used for beam management and report in one        resource set. Here, an SSB resource set can be set as {SSBx1,        SSBx2, SSBx3, SSBx4, . . . }. An SSB index can be defined in the        range of 0 to 63.    -   The UE receives the signals on SSB resources from the BS on the        basis of the CSI-SSB-ResourceSetList.    -   When CSI-RS reportConfig with respect to a report on SSBRI and        reference signal received power (RSRP) is set, the UE reports        the best SSBRI and RSRP corresponding thereto to the BS. For        example, when reportQuantity of the CSI-RS reportConfig IE is        set to ‘ssb-Index-RSRP’, the UE reports the best SSBRI and RSRP        corresponding thereto to the BS.

When a CSI-RS resource is configured in the same OFDM symbols as an SSBand ‘QCL-TypeD’ is applicable, the UE can assume that the CSI-RS and theSSB are quasi co-located (QCL) from the viewpoint of ‘QCL-TypeD’. Here,QCL-TypeD may mean that antenna ports are quasi co-located from theviewpoint of a spatial Rx parameter. When the UE receives signals of aplurality of DL antenna ports in a QCL-TypeD relationship, the same Rxbeam can be applied.

Next, a DL BM procedure using a CSI-RS will be described.

An Rx beam determination (or refinement) procedure of a UE and a Tx beamswiping procedure of a BS using a CSI-RS will be sequentially described.A repetition parameter is set to ‘ON’ in the Rx beam determinationprocedure of a UE and set to ‘OFF’ in the Tx beam swiping procedure of aBS.

First, the Rx beam determination procedure of a UE will be described.

-   -   The UE receives an NZP CSI-RS resource set IE including an RRC        parameter with respect to ‘repetition’ from a BS through RRC        signaling. Here, the RRC parameter ‘repetition’ is set to ‘ON’.    -   The UE repeatedly receives signals on resources in a CSI-RS        resource set in which the RRC parameter ‘repetition’ is set to        ‘ON’ in different OFDM symbols through the same Tx beam (or DL        spatial domain transmission filters) of the BS.    -   The UE determines an RX beam thereof    -   The UE skips a CSI report. That is, the UE can skip a CSI report        when the RRC parameter ‘repetition’ is set to ‘ON’.        -   Next, the Tx beam determination procedure of a BS will be            described.    -   A UE receives an NZP CSI-RS resource set IE including an RRC        parameter with respect to ‘repetition’ from the BS through RRC        signaling. Here, the RRC parameter ‘repetition’ is related to        the Tx beam swiping procedure of the BS when set to ‘OFF’.    -   The UE receives signals on resources in a CSI-RS resource set in        which the RRC parameter ‘repetition’ is set to ‘OFF’ in        different DL spatial domain transmission filters of the BS.    -   The UE selects (or determines) a best beam.    -   The UE reports an ID (e.g., CRI) of the selected beam and        related quality information (e.g., RSRP) to the BS. That is,        when a CSI-RS is transmitted for BM, the UE reports a CRI and        RSRP with respect thereto to the BS.

Next, the UL BM procedure using an SRS will be described.

-   -   A UE receives RRC signaling (e.g., SRS-Config IE) including a        (RRC parameter) purpose parameter set to ‘beam management” from        a BS. The SRS-Config IE is used to set SRS transmission. The        SRS-Config IE includes a list of SRS-Resources and a list of        SRS-ResourceSets. Each SRS resource set refers to a set of        SRS-resources.

The UE determines Tx beamforming for SRS resources to be transmitted onthe basis of SRS-SpatialRelation Info included in the SRS-Config IE.Here, SRS-SpatialRelation Info is set for each SRS resource andindicates whether the same beamforming as that used for an SSB, a CSI-RSor an SRS will be applied for each SRS resource.

-   -   When SRS-SpatialRelationInfo is set for SRS resources, the same        beamforming as that used for the SSB, CSI-RS or SRS is applied.        However, when SRS-SpatialRelationInfo is not set for SRS        resources, the UE arbitrarily determines Tx beamforming and        transmits an SRS through the determined Tx beamforming.

Next, a beam failure recovery (BFR) procedure will be described.

In a beamformed system, radio link failure (RLF) may frequently occurdue to rotation, movement or beamforming blockage of a UE. Accordingly,NR supports BFR in order to prevent frequent occurrence of RLF. BFR issimilar to a radio link failure recovery procedure and can be supportedwhen a UE knows new candidate beams. For beam failure detection, a BSconfigures beam failure detection reference signals for a UE, and the UEdeclares beam failure when the number of beam failure indications fromthe physical layer of the UE reaches a threshold set through RRCsignaling within a period set through RRC signaling of the BS. Afterbeam failure detection, the UE triggers beam failure recovery byinitiating a random access procedure in a PCell and performs beamfailure recovery by selecting a suitable beam. (When the BS providesdedicated random access resources for certain beams, these areprioritized by the UE). Completion of the aforementioned random accessprocedure is regarded as completion of beam failure recovery.

D. URLLC (Ultra-Reliable and Low Latency Communication)

URLLC transmission defined in NR can refer to (1) a relatively lowtraffic size, (2) a relatively low arrival rate, (3) extremely lowlatency requirements (e.g., 0.5 and 1 ms), (4) relatively shorttransmission duration (e.g., 2 OFDM symbols), (5) urgentservices/messages, etc. In the case of UL, transmission of traffic of aspecific type (e.g., URLLC) needs to be multiplexed with anothertransmission (e.g., eMBB) scheduled in advance in order to satisfy morestringent latency requirements. In this regard, a method of providinginformation indicating preemption of specific resources to a UEscheduled in advance and allowing a URLLC UE to use the resources for ULtransmission is provided.

NR supports dynamic resource sharing between eMBB and URLLC. eMBB andURLLC services can be scheduled on non-overlapping time/frequencyresources, and URLLC transmission can occur in resources scheduled forongoing eMBB traffic. An eMBB UE may not ascertain whether PDSCHtransmission of the corresponding UE has been partially punctured andthe UE may not decode a PDSCH due to corrupted coded bits. In view ofthis, NR provides a preemption indication. The preemption indication mayalso be referred to as an interrupted transmission indication.

With regard to the preemption indication, a UE receivesDownlinkPreemption IE through RRC signaling from a BS. When the UE isprovided with DownlinkPreemption IE, the UE is configured with INT-RNTIprovided by a parameter int-RNTI in DownlinkPreemption IE for monitoringof a PDCCH that conveys DCI format 2_1. The UE is additionallyconfigured with a corresponding set of positions for fields in DCIformat 2_1 according to a set of serving cells and positionInDCI byINT-ConfigurationPerServing Cell including a set of serving cell indexesprovided by servingCellID, configured having an information payload sizefor DCI format 2_1 according to dci-Payloadsize, and configured withindication granularity of time-frequency resources according totimeFrequency Sect.

The UE receives DCI format 2_1 from the BS on the basis of theDownlinkPreemption IE.

When the UE detects DCI format 2_1 for a serving cell in a configuredset of serving cells, the UE can assume that there is no transmission tothe UE in PRBs and symbols indicated by the DCI format 2_1 in a set ofPRBs and a set of symbols in a last monitoring period before amonitoring period to which the DCI format 2_1 belongs. For example, theUE assumes that a signal in a time-frequency resource indicatedaccording to preemption is not DL transmission scheduled therefor anddecodes data on the basis of signals received in the remaining resourceregion.

E. mMTC (Massive MTC)

mMTC (massive Machine Type Communication) is one of 5G scenarios forsupporting a hyper-connection service providing simultaneouscommunication with a large number of UEs. In this environment, a UEintermittently performs communication with a very low speed andmobility. Accordingly, a main goal of mMTC is operating a UE for a longtime at a low cost. With respect to mMTC, 3GPP deals with MTC and NB(NarrowBand)-IoT.

mMTC has features such as repetitive transmission of a PDCCH, a PUCCH, aPDSCH (physical downlink shared channel), a PUSCH, etc., frequencyhopping, retuning, and a guard period.

That is, a PUSCH (or a PUCCH (particularly, a long PUCCH) or a PRACH)including specific information and a PDSCH (or a PDCCH) including aresponse to the specific information are repeatedly transmitted.Repetitive transmission is performed through frequency hopping, and forrepetitive transmission, (RF) retuning from a first frequency resourceto a second frequency resource is performed in a guard period and thespecific information and the response to the specific information can betransmitted/received through a narrowband (e.g., 6 resource blocks (RBs)or 1 RB).

F. Basic Operation Between Autonomous Vehicles Using 5G Communication

FIG. 4 shows an example of basic operations of an autonomous vehicle anda 5G network in a 5G communication system.

The autonomous vehicle transmits specific information to the 5G network(S1). The specific information may include autonomous driving relatedinformation. In addition, the 5G network can determine whether toremotely control the vehicle (S2). Here, the 5G network may include aserver or a module which performs remote control related to autonomousdriving. In addition, the 5G network can transmit information (orsignal) related to remote control to the autonomous vehicle (S3).

G. Applied Operations Between Autonomous Vehicle and 5G Network in 5GCommunication System

Hereinafter, the operation of an autonomous vehicle using 5Gcommunication will be described in more detail with reference towireless communication technology (BM procedure, URLLC, mMTC, etc.)described in FIGS. 2 and 3.

First, a basic procedure of an applied operation to which a methodproposed by the present disclosure which will be described later andeMBB of 5G communication are applied will be described.

As in steps S1 and S3 of FIG. 4, the autonomous vehicle performs aninitial access procedure and a random access procedure with the 5Gnetwork prior to step S1 of FIG. 4 in order to transmit/receive signals,information and the like to/from the 5G network.

More specifically, the autonomous vehicle performs an initial accessprocedure with the 5G network on the basis of an SSB in order to acquireDL synchronization and system information. A beam management (BM)procedure and a beam failure recovery procedure may be added in theinitial access procedure, and quasi-co-location (QCL) relation may beadded in a process in which the autonomous vehicle receives a signalfrom the 5G network.

In addition, the autonomous vehicle performs a random access procedurewith the 5G network for UL synchronization acquisition and/or ULtransmission. The 5G network can transmit, to the autonomous vehicle, aUL grant for scheduling transmission of specific information.Accordingly, the autonomous vehicle transmits the specific informationto the 5G network on the basis of the UL grant. In addition, the 5Gnetwork transmits, to the autonomous vehicle, a DL grant for schedulingtransmission of 5G processing results with respect to the specificinformation. Accordingly, the 5G network can transmit, to the autonomousvehicle, information (or a signal) related to remote control on thebasis of the DL grant.

Next, a basic procedure of an applied operation to which a methodproposed by the present disclosure which will be described later andURLLC of 5G communication are applied will be described.

As described above, an autonomous vehicle can receive DownlinkPreemptionIE from the 5G network after the autonomous vehicle performs an initialaccess procedure and/or a random access procedure with the 5G network.Then, the autonomous vehicle receives DCI format 2_1 including apreemption indication from the 5G network on the basis ofDownlinkPreemption IE. The autonomous vehicle does not perform (orexpect or assume) reception of eMBB data in resources (PRBs and/or OFDMsymbols) indicated by the preemption indication. Thereafter, when theautonomous vehicle needs to transmit specific information, theautonomous vehicle can receive a UL grant from the 5G network.

Next, a basic procedure of an applied operation to which a methodproposed by the present disclosure which will be described later andmMTC of 5G communication are applied will be described.

Description will focus on parts in the steps of FIG. 4 which are changedaccording to application of mMTC.

In step S1 of FIG. 4, the autonomous vehicle receives a UL grant fromthe 5G network in order to transmit specific information to the 5Gnetwork. Here, the UL grant may include information on the number ofrepetitions of transmission of the specific information and the specificinformation may be repeatedly transmitted on the basis of theinformation on the number of repetitions. That is, the autonomousvehicle transmits the specific information to the 5G network on thebasis of the UL grant. Repetitive transmission of the specificinformation may be performed through frequency hopping, the firsttransmission of the specific information may be performed in a firstfrequency resource, and the second transmission of the specificinformation may be performed in a second frequency resource. Thespecific information can be transmitted through a narrowband of 6resource blocks (RBs) or 1 RB.

The above-described 5G communication technology can be combined withmethods proposed in the present disclosure which will be described laterand applied or can complement the methods proposed in the presentdisclosure to make technical features of the methods concrete and clear.

FIG. 5 is a block diagram illustrating a mobile terminal related to thepresent disclosure. FIGS. 6 and 7 are conceptual views of a mobileterminal related to the present disclosure when viewed from differentdirections.

Referring to FIGS. 5, 6 and 7, a mobile terminal MP may include awireless communication unit 110, an input unit 120, a sensing unit 140,an output unit 150, an interface unit 160, a memory 170, a controller180, and a power supply unit 190, and the like. It is understood thatall the components illustrated in FIG. 5 is not requirements toimplement the mobile terminal, and that more or fewer components may bealternatively implemented.

More specifically, the wireless communication unit 110 may include oneor more modules which permit wireless communications between the mobileterminal MP and a wireless communication system, between the mobileterminal MP and another mobile terminal MP, or between the mobileterminal MP and an external server. Further, the wireless communicationunit 110 may include one or more modules which connect the mobileterminal MP to one or more 5G networks.

The wireless communication unit 110 may include at least one of abroadcast receiving module 111, a mobile communication module 112, awireless Internet module 113, a short-range communication module 114, ora location information module 115.

The input unit 120 may include a camera 121 which is one type of animage input unit for inputting an image signal, a microphone 122 whichis one type of an audio input unit for inputting an audio signal, and auser input unit 123 (e.g., touch key, push key, etc.) for allowing auser to input information. Audio data or image data obtained by theinput unit 120 may be analyzed and processed by user control commands.

The sensing unit 140 may include one or more sensors for sensing atleast one of internal information of the mobile terminal, informationabout a surrounding environment of the mobile terminal, and userinformation. For example, the sensing unit 140 may include at least oneof a proximity sensor 141, an illumination sensor 142, a touch sensor,an acceleration sensor, a magnetic sensor, a G-sensor, a gyroscopesensor, a motion sensor, an RGB sensor, an infrared (IR) sensor, afinger scan sensor, an ultrasonic sensor, an optical sensor (e.g.,camera 121), the microphone 122, a battery gauge, an environment sensor(e.g., a barometer, a hygrometer, a thermometer, a radiation detectionsensor, a thermal sensor, a gas sensor, etc.), and a chemical sensor(e.g., an electronic nose, a health care sensor, a biometric sensor,etc.). The mobile terminal disclosed in the present specification may beconfigured to combine and utilize information obtained from two or moresensors of the sensing unit 140.

The output unit 150 may be configured to output various types ofinformation, such as audio, video, tactile output, and the like. Theoutput unit 150 may include at least one of a display unit 151, an audiooutput unit 152, a haptic module 153, or an optical output unit 154. Thedisplay unit 151 may have an inter-layered structure or an integratedstructure with a touch sensor to implement a touch screen. The touchscreen may provide an output interface between the mobile terminal MPand the user, as well as function as the user input unit 123 whichprovides an input interface between the mobile terminal MP and the user.

The interface unit 160 serves as an interface with various types ofexternal devices that can be coupled to the mobile terminal MP. Theinterface unit 160 may include at least one of wired/wireless headsetports, external power supply ports, wired/wireless data ports, memorycard ports, ports for connecting a device having an identificationmodule, audio input/output (I/O) ports, video I/O ports, or earphoneports. The mobile terminal MP may perform assorted control functionsassociated with a connected external device, in response to the externaldevice being connected to the interface unit 160.

The memory 170 stores data to support various functions of the mobileterminal MP. For instance, the memory 170 may be configured to storemultiple application programs or applications executed in the mobileterminal MP, data or instructions for operations of the mobile terminalMP, and the like. At least some of these application programs may bedownloaded from an external server via wireless communication. Otherapplication programs may be installed within the mobile terminal MP attime of manufacturing or shipping, which is typically the case for basicfunctions (e.g., receiving a call, placing a call, receiving a message,sending a message, and the like) of the mobile terminal MP. Theapplication programs may be stored in the memory 170, installed in themobile terminal MP, and executed by the controller 180 to perform anoperation (or function) for the mobile terminal MP.

The controller 180 typically functions to control overall operation ofthe mobile terminal MP, in addition to the operations associated withthe application programs. The controller 180 may provide or processsuitable information or functions appropriate for the user by processingsignals, data, information and the like, which are input or output bythe components mentioned above, or activating application programsstored in the memory 170.

The controller 180 may control at least some of the componentsillustrated in FIG. 5 in order to execute an application program thathave been stored in the memory 170. In addition, the controller 180 maycombine and operate at least two of the components included in themobile terminal MP for the execution of the application program.

The power supply unit 190 is configured to receive external power orprovide internal power and supply power to the respective componentsincluded in the mobile terminal MP under the control of the controller180. The power supply unit 190 may include a battery, and the batterymay be configured to be embedded in the device body, or configured to bedetachable from the device body.

At least some of the above components may be combined with one anotherand operate, in order to implement an operation, a control, or a controlmethod of a mobile terminal according to various embodiments describedbelow. Further, the operation, the control, or the control method of themobile terminal according to various embodiments may be implemented onthe mobile terminal by an activation of at least one application programstored in the memory 170.

Referring to FIGS. 6 and 7, the mobile terminal MP includes a bar-shapedterminal body. However, the present disclosure is not limited theretoand may implement the mobile terminal MP in any of a variety ofdifferent configurations. Examples of such configurations includewatch-type, clip-type, glasses-type, or folder-type, flip-type,slide-type, swing-type, and swivel-type in which two and more bodies arecombined with each other in a relatively movable manner, andcombinations thereof. Discussion herein will often relate to aparticular type of mobile terminal. However, such teachings with regardto a particular type of mobile terminal will generally apply to othertypes of mobile terminals as well.

Here, the terminal body may be understood as a concept of referring tothe mobile terminal MP by considering the mobile terminal as at leastone aggregate.

The mobile terminal MP includes a case (e.g., frame, housing, cover,etc.) forming an appearance of the terminal. As illustrated, the mobileterminal MP may include a front case 101 and a rear case 102. Variouselectronic components are incorporated in an inner space formed bycoupling the front case 101 and the rear case 102. At least one middlecase may be additionally positioned between the front case 101 and therear case 102.

The display unit 151 may be located on a front surface of the terminalbody to output information. As illustrated, a window 151 a of thedisplay unit 151 may be mounted on the front case 101 to form the frontsurface of the terminal body together with the front case 101.

In some embodiments, electronic components may also be mounted on therear case 102. Examples of such electronic components mounted on therear case 102 include a detachable battery, an identification module, amemory card, and the like. In this case, a rear cover 103 covering theelectronic components may be detachably coupled to the rear case 102.Therefore, when the rear cover 103 is detached from the rear case 102,the electronic components mounted on the rear case 102 are externallyexposed.

As illustrated, when the rear cover 103 is coupled to the rear case 102,a portion of a side surface of the rear case 102 may be exposed. In somecases, upon the coupling, the rear case 102 may also be completelyshielded by the rear cover 103. In some embodiments, the rear cover 103may include an opening for externally exposing a camera 121 b or anaudio output module 152 b.

The cases 101, 102, and 103 may be formed by injection-molding asynthetic resin or may be formed of a metal, for example, stainlesssteel (STS), aluminum (Al), titanium (Ti), or the like.

As an alternative to the example in which the plurality of cases form aninner space for accommodating the various electronic components, themobile terminal MP may be configured such that one case forms the innerspace. In this example, a mobile terminal MP having a uni-body is formedin such a manner that synthetic resin or metal extends from a sidesurface to a rear surface.

The mobile terminal MP may include a waterproofing unit (not shown) forpreventing introduction of water into the terminal body. For example,the waterproofing unit may include a waterproofing member which islocated between the window 151 a and the front case 101, between thefront case 101 and the rear case 102, or between the rear case 102 andthe rear cover 103 to hermetically seal an inner space when those casesare coupled.

The mobile terminal MP may include the display unit 151, first andsecond audio output units 152 a and 152 b, the proximity sensor 141, theillumination sensor 142, the optical output module 154, first and secondcameras 121 a and 121 b, first to third manipulation units 123 a, 123 b,and 123 c, the microphone 122, the interface unit 160, an earphone jack130, and the like.

Hereinafter, as illustrated in FIGS. 6 and 7, as an example, the mobileterminal MP is shown configured such that the display unit 151, thefirst audio output unit 152 a, the proximity sensor 141, theillumination sensor 142, the optical output module 154, the first camera121 a, and the first manipulation unit 123 a are disposed on the frontsurface of the terminal body, the second manipulation unit 123 b, themicrophone 122, the earphone jack 130, and the interface unit 160 aredisposed on the side surface of the terminal body, and the second audiooutput unit 152 b, the third manipulation unit 123 c, and the secondcamera 121 b are disposed on the rear surface of the terminal body.

However, these components are not limited to these arrangements. In someembodiments, some components may be excluded or replaced, or may bedisposed on other surface. For example, the first manipulation unit 123a may not be disposed on the front surface of the terminal body, and thesecond audio output unit 152 b may be disposed on the side surface ofthe terminal body not the rear surface of the terminal body.

The display unit 151 displays (outputs) information processed in themobile terminal MP. For example, the display unit 151 may displayexecution screen information of an application program running in themobile terminal MP, or user interface (UI) and graphic user interface(GUI) information in response to the execution screen information.

The display unit 151 may include at least one of a liquid crystaldisplay (LCD), a thin film transistor-liquid crystal display (TFT-LCD),an organic light emitting diode (OLED) display, a flexible display, athree-dimensional (3D) display, or an e-ink display.

The display unit 151 may be implemented using two or more display unitsaccording to the implementation type of the mobile terminal MP. In thisinstance, a plurality of the display units may be disposed on onesurface of the mobile terminal MP to be either spaced apart from eachother or integrated, or the display units may be respectively disposedon different surfaces of the mobile terminal MP.

The display unit 151 may also include a touch sensor which senses atouch input received at the display unit 151 in order to receive acontrol command using a touching manner. If a touch is input to thedisplay unit 151, the touch sensor may be configured to sense the touch,and the controller 180 may be configured to generate a control commandcorresponding to the touch. The content which is input in the touchingmanner may be a text or numerical value, or a menu item which can beindicated or designated in various modes.

The touch sensor may be formed in a film type having a touch pattern anddisposed between the window 151 a and a display (not shown) on a rearsurface of the window 151 a, or may be a metal wire which is patterneddirectly on the rear surface of the window 151 a. Alternatively, thetouch sensor may be integrally formed with the display. For example, thetouch sensor may be disposed on a substrate of the display or within thedisplay.

As described above, the display unit 151 may also form a touch screentogether with the touch sensor. In this case, the touch screen may serveas the user input unit 123 (see FIG. 5). In some cases, the touch screenmay replace at least a part of function of the first manipulation unit123 a.

The first audio output module 152 a may be implemented as a receiverwhich transmits a call sound to user's ears, and the second audio outputmodule 152 b may be implemented in the form of a loud speaker to outputvarious alarm sounds or multimedia reproduction sounds.

The window 151 a of the display unit 151 may include an audio hole whichpermits audio generated by the first audio output module 152 a to pass.However, the present disclosure is not limited thereto, and onealternative is to allow audio to be released along an assembly gapbetween structural bodies (for example, a gap between the window 151 aand the front case 101). In this case, a hole independently formed tooutput audio sounds may be invisible or is otherwise hidden in terms ofappearance, thereby further simplifying the appearance and manufacturingof the mobile terminal MP.

The optical output unit 154 is configured to output light for indicatingthat an event has occurred. Examples of the events include a messagereception, a call signal reception, a missed call, an alarm, a schedulenotice, an email reception, information reception through anapplication, and the like. When a user has checked a generated event,the controller 180 may control the optical output unit 154 to stop thelight output.

The first camera 121 a processes image frames of as a still image or amoving image obtained by an image sensor in a capture mode or a videocall mode. The processed image frames may then be displayed on thedisplay unit 151 or stored in the memory 170.

The first to third manipulation units 123 a, 123 b and 123 b areexamples of the user input unit 123, which is manipulated by a user toprovide an input to the mobile terminal MP, and may also be referredcommonly to as a manipulating portion. The first to third manipulationunits 123 a, 123 b and 123 b may employ any tactile method that allowsthe user to perform manipulation, such as touch, push, scroll, or thelike. The first and second manipulation units 123 a and 123 b may alsoemploy any non-tactile method that allows the user to performmanipulation such as proximity touch, hovering touch, or the like. Thethird manipulation unit 123 c includes a finger scan sensor and canobtain user's fingerprint. The obtained fingerprint may be provided tothe controller 180.

This figure illustrates the first manipulation unit 123 a as a touchkey, but the present disclosure is not limited thereto. For example,possible alternatives of the first manipulation unit 123 a include amechanical key, a push key, a touch key, and combinations thereof.

Input received at the first and second manipulation units 123 a and 123b may be set in various ways. For example, the first manipulation unit123 a may be used by the user to provide an input to a menu, home key,cancel, search, or the like, and the second manipulation unit 123 b maybe used by the user to provide an input to control a volume level beingoutput from the first or second audio output unit 152 a or 152 b, toswitch to a touch recognition mode of the display unit 151, or the like.

As another example of the user input unit 123, the third manipulationunit 123 c may be located on the rear surface of the terminal body. Thethird manipulation unit 123 c may be manipulated by a user to provideinput to the mobile terminal MP. The input may be set in a variety ways.

For example, the third manipulation unit 123 c may be used by the userto provide an input for power on/off, start, end, scroll, control volumelevel being output from the first and second audio output units 152 aand 152 b, switch to a touch recognition mode of the display unit 151,fingerprint information acquisition, and the like. The rear input unitmay be configured to permit a touch input, a push input, or combinationsthereof.

The rear input unit may be located to overlap the display unit 151 ofthe front side in a thickness direction of the terminal body. As anexample, the rear input unit may be located on an upper end portion ofthe rear side of the terminal body such that the user can easilymanipulate it using a forefinger when the user grabs the terminal bodywith one hand. However, the present disclosure is not limited thereto.Alternatively, a position of the rear input unit may be changed.

If the rear input unit is positioned on the rear surface of the terminalbody as described above, a new type of user interface using the rearinput unit can be implemented. If the first manipulation unit 123 a isomitted from the front surface of the terminal body by replacing atleast some functions of the first manipulation unit 123 a on the frontsurface of the terminal body by the touch screen or the rear input unitdescribed above, the display unit 151 can have a larger screen.

As a further alternative, the mobile terminal MP may include a fingerscan sensor which scans a user's fingerprint. The controller 180 can usefingerprint information sensed by the finger scan sensor as anauthentication procedure. The finger scan sensor may also be embedded inthe display unit 151 or the user input unit 123.

The microphone 122 is configured to receive user's voice, other sounds,and the like. The microphone 122 may be implemented using a plurality ofmicrophones and configured to receive stereo sounds.

The interface unit 160 serves as a path allowing the mobile terminal MPto interface with external devices. For example, the interface unit 160may include at least one of a connection terminal for connecting toanother device (e.g., an earphone, an external speaker, etc.), a portfor short-range communication (e.g., an infrared data association (IrDA)port, a Bluetooth port, a wireless LAN port, etc.), or a power supplyterminal for supplying power to the mobile terminal MP. The interfaceunit 160 may be implemented in the form of a socket for accommodating anexternal card, such as subscriber identification module (SIM), useridentity module (UIM), or a memory card for information storage.

The second camera 121 b may be located at the rear surface of theterminal body. In this instance, the second camera 121 b has an imagecapturing direction that is substantially opposite to an image capturingdirection of the first camera unit 121 a.

The second camera 121 b may include a plurality of lenses arranged alongat least one line. The plurality of lenses may also be arranged in amatrix form. The cameras may be referred to as an “array camera.” Whenthe second camera 121 b is implemented as an array camera, the secondcamera 121 b can take images using the plurality of lenses in variousmanners and thus can obtain the images with better quality.

A flash 124 may be positioned adjacent to the second camera 121 b. Whena subject is taken with the second camera 121 b, the flash 124illuminates the subject.

The second audio output module 152 b may be additionally located on theterminal body. The second audio output module 152 b may implementstereophonic sound functions in conjunction with the first audio outputmodule 152 a, and may be also used for implementing a speaker phone modefor call communication.

At least one antenna for wireless communication may be located on theterminal body. The antenna may be embedded in the terminal body orformed at the case. For example, the antenna which forms a part of thebroadcast receiving module 111 (see FIG. 5) may be configured to beretractable into the terminal body. Alternatively, the antenna may beformed in a film type and attached to an inner surface of the rear cover103, or may be replaced by a case including a conductive material.

The power supply unit 190 (see FIG. 5) for supplying power to the mobileterminal MP is located at the terminal body. The power supply unit 190may include a battery 191 that is embedded in the terminal body and isdetachably configured to the outside of the terminal body.

The battery 191 may be configured to receive power via a power cableconnected to the interface unit 160. The battery 191 may also beconfigured to be charged using a wireless charger. The wireless chargingmay be implemented by a magnetic induction method or a resonance method(electromagnetic resonance method).

This figure illustrates that the rear cover 103 is configured to coupleto the rear case 102 for covering the battery 191 to thereby prevent theseparation of the battery 191 and to protect the battery 191 from anexternal impact or foreign material, by way of example. When the battery191 is detachable from the terminal body, the rear cover 103 may bedetachably coupled to the rear case 102.

An accessory for protecting an appearance or assisting or extending thefunctions of the mobile terminal MP may be additionally provided to themobile terminal MP. Examples of the accessory may include a cover or apouch for covering or accommodating at least one surface of the mobileterminal MP. The cover or the pouch may be configured to cooperate withthe display unit 151 and extend the function of the mobile terminal MP.Another example of the accessory may include a touch pen for assistingor extending a touch input to a touch screen.

FIG. 8 is a block diagram of an AI device in accordance with theembodiment of the present disclosure.

The AI device 20 may include electronic equipment that includes an AImodule to perform AI processing or a server that includes the AI module.Furthermore, the AI device 20 may be included in at least a portion ofthe artificial intelligent devices illustrated in FIG. 7, and may beprovided to perform at least some of the AI processing.

The AI processing may include all operations related to the function ofthe artificial intelligent devices illustrated in FIG. 4. For example,the intelligent robot cleaner may AI-process sensing data or travel datato perform processing/determining and a control-signal generatingoperation. Furthermore, for example, the intelligent robot cleaner mayAI-process data acquired through interaction with other electronicequipment provided in the intelligent robot cleaner to control sensing.

The AI device 20 may include an AI processor 21, a memory 25 and/or acommunication unit 27.

The AI device 20 may be a computing device capable of learning a neuralnetwork, and may be implemented as various electronic devices such as aserver, a desktop PC, a laptop PC or a tablet PC.

The AI processor 21 may learn the neural network using a program storedin the memory 25. Particularly, the AI processor 21 may learn the neuralnetwork for recognizing data related to the artificial intelligentdevices. Here, the neural network for recognizing data related to theartificial intelligent devices may be designed to simulate a human brainstructure on the computer, and may include a plurality of network nodeshaving weights that simulate the neurons of the human neural network.The plurality of network nodes may exchange data according to theconnecting relationship to simulate the synaptic action of neurons inwhich the neurons exchange signals through synapses. Here, the neuralnetwork may include the deep learning model developed from the neuralnetwork model. While the plurality of network nodes is located atdifferent layers in the deep learning model, the nodes may exchange dataaccording to the convolution connecting relationship. Examples of theneural network model include various deep learning techniques, such as adeep neural network (DNN), a convolution neural network (CNN), arecurrent neural network (RNN, Recurrent Boltzmann Machine), arestricted Boltzmann machine (RBM,), a deep belief network (DBN) or adeep Q-Network, and may be applied to fields such as computer vision,voice recognition, natural language processing, voice/signal processingor the like.

Meanwhile, the processor performing the above-described function may bea general-purpose processor (e.g. CPU), but may be an AI dedicatedprocessor (e.g. GPU) for artificial intelligence learning.

The memory 25 may store various programs and data required to operatethe AI device 20. The memory 25 may be implemented as a non-volatilememory, a volatile memory, a flash memory), a hard disk drive (HDD) or asolid state drive (SDD). The memory 25 may be accessed by the AIprocessor 21, and reading/writing/correcting/deleting/update of data bythe AI processor 21 may be performed.

Furthermore, the memory 25 may store the neural network model (e.g. thedeep learning model 26) generated through a learning algorithm forclassifying/recognizing data in accordance with the embodiment of thepresent disclosure.

The AI processor 21 may include a data learning unit 22 which learns theneural network for data classification/recognition. The data learningunit 22 may learn a criterion about what learning data is used todetermine the data classification/recognition and about how to classifyand recognize data using the learning data. The data learning unit 22may learn the deep learning model by acquiring the learning data that isused for learning and applying the acquired learning data to the deeplearning model.

The data learning unit 22 may be made in the form of at least onehardware chip and may be mounted on the AI device 20. For example, thedata learning unit 22 may be made in the form of a dedicated hardwarechip for the artificial intelligence AI, and may be made as a portion ofthe general-purpose processor (CPU) or the graphic dedicated processor(GPU) to be mounted on the AI device 20. Furthermore, the data learningunit 22 may be implemented as a software module. When the data learningunit is implemented as the software module (or a program moduleincluding instructions), the software module may be stored in anon-transitory computer readable medium. In this case, at least onesoftware module may be provided by an operating system (OS) or anapplication.

The data learning unit 22 may include the learning-data acquisition unit23 and the model learning unit 24.

The learning-data acquisition unit 23 may acquire the learning dataneeded for the neural network model for classifying and recognizing thedata. For example, the learning-data acquisition unit 23 may acquirevehicle data and/or sample data which are to be inputted into the neuralnetwork model, as the learning data.

The model learning unit 24 may learn to have a determination criterionabout how the neural network model classifies predetermined data, usingthe acquired learning data. The model learning unit 24 may learn theneural network model, through supervised learning using at least some ofthe learning data as the determination criterion. Alternatively, themodel learning unit 24 may learn the neural network model throughunsupervised learning that finds the determination criterion, bylearning by itself using the learning data without supervision.Furthermore, the model learning unit 24 may learn the neural networkmodel through reinforcement learning using feedback on whether theresult of situation determination according to the learning is correct.Furthermore, the model learning unit 24 may learn the neural networkmodel using the learning algorithm including error back-propagation orgradient descent.

If the neural network model is learned, the model learning unit 24 maystore the learned neural network model in the memory. The model learningunit 24 may store the learned neural network model in the memory of theserver connected to the AI device 20 with a wire or wireless network.

The data learning unit 22 may further include a learning-datapreprocessing unit (not shown) and a learning-data selection unit (notshown) to improve the analysis result of the recognition model or tosave resources or time required for generating the recognition model.

The learning-data preprocessing unit may preprocess the acquired data sothat the acquired data may be used for learning for situationdetermination. For example, the learning-data preprocessing unit mayprocess the acquired data in a preset format so that the model learningunit 24 may use the acquired learning data for learning for imagerecognition.

Furthermore, the learning-data selection unit may select the datarequired for learning among the learning data acquired by thelearning-data acquisition unit 23 or the learning data preprocessed inthe preprocessing unit. The selected learning data may be provided tothe model learning unit 24. For example, the learning-data selectionunit may select only data on the object included in a specific region asthe learning data, by detecting the specific region in the imageacquired by the camera of the artificial intelligent devices.

Furthermore, the data learning unit 22 may further include a modelevaluation unit (not shown) to improve the analysis result of the neuralnetwork model.

When the model evaluation unit inputs evaluated data into the neuralnetwork model and the analysis result outputted from the evaluated datadoes not satisfy a predetermined criterion, the model learning unit 22may learn again. In this case, the evaluated data may be predefined datafor evaluating the recognition model. By way of example, the modelevaluation unit may evaluate that the predetermined criterion is notsatisfied when the number or ratio of the evaluated data in which theanalysis result is inaccurate among the analysis result of the learnedrecognition model for the evaluated data exceeds a preset threshold.

The communication unit 27 may transmit the AI processing result by theAI processor 21 to the external electronic equipment.

Here, the external electronic equipment may be defined as the artificialintelligent devices. Furthermore, the AI device 20 may be defined asanother artificial intelligent devices or a 5G network that communicateswith the artificial intelligent devices. Meanwhile, the AI device 20 maybe implemented by being functionally embedded in an autonomous drivingmodule provided in the artificial intelligent devices. Furthermore, the5G network may include a server or a module that performs relatedcontrol of the artificial intelligent devices.

Although the AI device 20 illustrated in FIG. 8 is functionally dividedinto the AI processor 21, the memory 25, the communication unit 27 andthe like, it is to be noted that the above-described components areintegrated into one module, which is referred to as an AI module.

FIG. 9 illustrates an example of an artificial neural network modelrelated to the present disclosure.

More specifically, FIG. 9(a) illustrates a general structure of theartificial neural network model, and FIG. 9(b) illustrates anautoencoder, that performs decoding after encoding and goes through areconstruction step, among the artificial neural network model.

The artificial neural network model may generally include an inputlayer, a hidden layer, and an output layer, and neurons included in eachlayer may be connected through weight values. The artificial neuralnetwork model may be configured to approximate a complex functionthrough a linear combination and a nonlinear activation function of theweight values and neuron values. The purpose of learning the artificialneural network model is to find a weight value for minimizing adifference between an output calculated at the output layer and a realoutput.

A deep neural network may mean an artificial neural network modelconsisting of several hidden layers between the input layer and theoutput layer. A neural network structure, in which complex nonlinearrelationships can be modeled by using many hidden layers, and advancedabstraction is possible by increasing the number of layers as describedabove, is referred to as deep learning. The deep learning learns a verylarge amount of data and thus can choose probably the highest answerbased on a result of learning if new data is input. Thus, the deeplearning can operate adaptively based on the input and can automaticallyfind feature factors in a process of learning a model based on data.

A deep learning based model may include various deep learning techniquessuch as deep neural networks (DNN), convolutional deep neural networks(CNN), recurrent Boltzmann machine (RNN), restricted Boltzmann machine(RBM), deep belief networks (DBN), and deep Q-network described abovewith reference to FIG. 8, but is not limited thereto. Further, a machinelearning method may be used in addition to the deep learning. Forexample, when features of input data are extracted by applying the deeplearning based model, and input data is classified or recognized basedon the extracted features, a machine learning based model can beapplied. The machine learning based model may include support vectormachine (SVM), AdaBoost, and the like, but is not limited thereto.

Referring to FIG. 9(a), an artificial neural network model according toan embodiment of the present disclosure may include an input layer, ahidden layer, an output layer, and a weight value. For example, FIG.9(a) illustrates a structure of an artificial neural network model inwhich a size of an input layer is 3, a size of each of first and secondhidden layers is 4, and a size of an output layer is 1. In thisinstance, neurons included in the hidden layer may be connected toneurons included in the input layer through a linear combination with anindividual weight value included in the weight value. Neurons includedin the output layer may be connected to neurons included in the hiddenlayer through a linear combination with an individual weight valueincluded in the weight value. The artificial neural network model canderive a model for minimizing a difference between an output calculatedat the output layer and a real output.

Referring to FIG. 9(b), an artificial neural network model according toan embodiment of the present disclosure may include an autoencoder. Ifthe autoencoder inputs original data to the artificial neural networkmodel, encodes the input data, and decodes the encoded data to therebyreconstruct data, there may be a slight difference between thereconstruction data and the input data, and the autoencoder canreconstruct data or evaluate reliability of input data based on thedifference. For example, the autoencoder illustrated in FIG. 9(b) isconfigured such that a size of an input layer and a size of an outputlayer are 5 and are the same, a size of a first hidden layer is 3, asize of a second hidden layer is 2, and a size of a third hidden layeris 3. That is, the number of nodes of the hidden layer graduallydecreases as it goes to the middle layer, and gradually increases as itapproaches the output layer. The autoencoder illustrated in FIG. 9(b) ismerely an example, and embodiments are not limited thereto. Theautoencoder may compare an input value of original data with an outputvalue of reconstruction data to determine that corresponding data is notlearned if there is a large difference between the input value and theoutput value and to determine that corresponding data has been alreadylearned if there is a small difference between them. Thus, reliabilityof input data can be improved by using the autoencoder.

A learned artificial neural network model according to an embodiment ofthe present disclosure applies information about a subject as learningdata. In this case, the information about the subject may include adistance between the subject and a camera, the number of subjects, awidth between a plurality of subjects, a distance between a plurality ofsubjects, background information behind the subject, and the like. Theartificial neural network model repeatedly learned several times maystop learning when an error value is less than a reference value and maybe stored in a memory of an AI device. When using the learned artificialneural network model, if information about a subject is input, theintelligent device may calculate distortion state information of thephotographed subject based on the information.

FIG. 10 is a flowchart illustrating a method of correcting an image ofan intelligent device according to one embodiment of the presentdisclosure.

A method of correcting an image of an intelligent device according to anembodiment of the present disclosure may be implemented in anintelligent device including the function described with reference toFIGS. 1 to 9. More specifically, a method of correcting an image of anintelligent device according to an embodiment of the present disclosuremay be implemented in the intelligent device 10 described with referenceto FIGS. 5 to 8.

The processor 180 may obtain subject state information from an image(S110). The subject state information may include information about aplurality of subjects, background information of the subject, andpreview image information before being photographed.

The processor 180 may obtain subject state information from an imagephotographed by at least one camera received in the intelligent device.For example, the camera may be a time of flight (TOP) camera. TOP may bea method of shooting light and measuring a time of reflected light tocalculate a distance. That is, the TOP camera may be a camera thatoutputs a distance image using a TOP method.

The processor 180 may analyze an image obtained from the camera toobtain a distance between a subject and a camera, the number ofsubjects, a width between a plurality of subjects, a distance between aplurality of subjects, background information behind the subject, andthe like. Further, the processor 180 may obtain background informationof the subject through a photographed preview image before beingphotographed.

The processor 180 may determine a distortion state of a size of thesubject (S130).

For example, the distortion state may occur due to the followingphenomenon. The subject photographed by the camera may be enlargedtoward the outside. In other words, when a size of the subject is thesame and a distance between lenses is the same, an image may be enlargedtoward the outside. For example, when a person is photographed tightlyin the lens, a head and foot may be photographed larger than an actualfigure and a body may be photographed smaller than an actual figure dueto distortion. However, when the head is put at the center of the lensand the foot is put at the bottom of the lens, the head may bephotographed relatively small and the legs may be photographed long.Further, the distortion state may be aggravated by perspective thattakes closer a near location and takes farther a far location. Forexample, when a person is photographed at a close distance, a shouldermay be represented narrowly and a head may be represented largely due todistortion. However, when photographing is performed at a distance morethan a predetermined distance, photographing may be performed similar toan actual appearance of the user.

The processor 180 may determine a distortion state of a size of thesubject based on the obtained information on the subject and thebackground. A detailed process of determining a distortion state of asize of the subject will be described later with reference to FIG. 11.As described above, determination of the distortion state of the size ofthe subject based on the subject state information may be performed inthe intelligent device 10 or in a 5G network.

When the processor 180 recognizes a distortion state of the size of thesubject, the processor 180 may correct the distortion state (S150). Whenthe distortion state of the size of the subject is recognized, theprocessor 180 may check a distance between the camera and the pluralityof subjects and correct a distortion phenomenon caused by the distance.For example, when a distance between the camera and a first subject is afirst distance and a distance between the camera and a second subject isa second distance, by correcting the size of the first subject or thesecond subject in consideration of an interval between the firstdistance and the second distance, the processor 180 may eliminate adistortion phenomenon of the size generated between the first subjectand the second subject.

The processor 180 may extract a reference point from a subjectphotographed through the camera. The subject may be a person or a faceof a person. For example, the processor 180 may extract reference points(eyes, eyebrows, lips, glasses, etc.) from a face image. The processor180 may detect a size of the face on a face coordinate system (x, y, z)generated based on the extracted reference point. The processor 180 maydetect a size of each face based on a reference point of each of aplurality of face images, and calculate the size of each detected faceand a distance between the camera and the face, thereby extractingdistortion of the size of the face according to the distance.

The processor 180 may correct a background by the corrected size of thesubject (S170). When the size of the subject is adjusted, the processor180 may correct a background generated by the adjusted subject based ona preview image or a periphery of the subject. A detailed process ofcorrecting the background generated by the corrected size of the subjectwill be described later with reference to FIG. 13.

FIG. 11 is a flowchart illustrating an example of determining adistortion state of a size of a subject in one embodiment of the presentdisclosure.

Referring to FIG. 11, in order to determine a distortion state of a sizeof the subject, the processor 180 may extract feature values fromsubject state information obtained through at least one camera (S131).

For example, the processor 180 may receive subject state information inwhich a subject is photographed from at least one camera. The processor180 may extract a feature value from the subject state information. Thefeature value specifically represents a distortion state of the size ofthe photographed subject among at least one feature that may beextracted from the subject state information.

The processor 180 may control the feature values to input to anartificial neural network (ANN) classifier trained to distinguishwhether a size of the subject is in a distorted state (S133).

The processor 180 may combine the extracted feature values to generate adistortion detection input. The distortion detection input may be inputto the ANN classifier trained to distinguish whether a size of thesubject is in a normal state or in a distorted state based on theextracted feature value.

The processor 180 may analyze an output value of the ANN (S135) anddetermine whether a size of the subject is in a distorted state based onthe output value of the ANN (S137).

The processor 180 may identify a distortion state of a size of thesubject from an output of the ANN classifier.

In FIG. 11, an example in which an operation of identifying a distortionstate of a size of the subject through AI processing is implemented inprocessing of the intelligent device 10 has been described, but thepresent disclosure is not limited thereto. For example, the AIprocessing may be performed on a 5G network based on sensing informationreceived from the intelligent device 10.

FIG. 12 is a flowchart illustrating another example of determining adistortion state of a size of a subject in one embodiment of the presentdisclosure.

The processor 180 may control a communication unit to transmit thesubject state information to an AI processor included in a 5G network.Further, the processor 180 may control the communication unit to receiveAI processed information from the AI processor.

The AI processed information may be information in which the size of thesubject is determined to any one of a normal state or a distorted state.

In order to transmit subject state information to the 5G network, theintelligent device 10 may perform an initial access procedure with the5G network. The intelligent device 10 may perform an initial accessprocedure with the 5G network based on a synchronization signal block(SSB).

Further, the intelligent device 10 may receive, from a network, downlinkcontrol information (DCI) used for scheduling transmission of thesubject state information obtained from at least one camera providedinside the intelligent device 10 through a wireless communication unit.

The processor 180 may transmit the subject state information to thenetwork based on the DCI.

The status information of the subject may be transmitted to the networkthrough a physical uplink shared channel (PUSCH), and DM-RSs of thePUSCH and the SSB may be QCL for QCL type D.

Referring to FIG. 12, the intelligent device 10 may transmit a featurevalue extracted from the subject state information to a 5G network(S300).

Here, the 5G network may include an AI processor or an AI system, andthe AI system of the 5G network may perform AI processing based on thereceived subject state information (S310).

The AI system may input feature values received from the intelligentdevice 10 to the ANN classifier (S311). The AI system may analyze an ANNoutput value (S313) and determine a distortion state of the subject sizefrom the ANN output value (S315). The 5G network may transmit distortionstate information of the subject size determined in the AI system to theintelligent device 10 through the wireless communication unit (S320).

The AI system determines that the size of the subject is in a distortedstate (S317), and if the size of the subject is in a distorted state,the AI system may calculate a size of each detected face and a distancebetween the camera and the face, thereby extracting distortion stateinformation on the size of the face according to the distance.

When a size of the subject is in a distorted state, the AI system maydetermine correction (S319). Further, the AI system may transmitinformation (or signal) related to correction to the intelligent device10 (S330).

The intelligent device 10 may transmit only subject state information tothe 5G network, and extract a feature value corresponding to adistortion detection input to be used as an input of an ANN fordetermining a distortion state of a subject size from the subject stateinformation in the AI system included in the 5G network.

FIG. 13 is a flowchart illustrating a method of correcting a backgroundof a subject according to one embodiment of the present disclosure.

Referring to FIG. 13, when it is determined that a size of the subjectis in a distorted state, the processor 180 may correct a size of thesubject (S150). As described above, the processor 180 may calculate asize of each detected subject and a distance between the camera and thesubject, extract distortion state information on the subject sizeaccording to the distance based on the size and the distance, andcorrect the subject size using the extracted distortion stateinformation.

When a blank occurs by the corrected subject size, the processor 180 maydetermine or recognize whether there is a background in the blank(S171).

If there is a background in the blank, the processor 180 may correct thebackground of the subject using a preview image (S172).

For example, the processor 180 may store a preview image, which is animage before the subject is photographed, and correct a blank using thestored preview image. The processor 180 may store or temporarily store aframe for a predetermined time from the moment when a camera is turnedon to take a selfie. The preview image may be an image photographed for0 seconds to 5 seconds before photographing the subject.

The processor 180 may obtain background information of the blank throughthe preview image and correct the background in the blank using theobtained background information of the blank. For example, in order toreduce an area in which the blank is generated, the processor 180 mayfill in an empty portion using frames stored in the preview image.

If there is no background in the blank, the processor 180 may correctthe blank using a surrounding background of the subject (S173).

For example, by combining a surrounding background of the subject andoutlines of the subject, the processor 180 may generate a background inthe blank. The blank background may be generated by combining colorinformation of the subject and color information of the surroundingbackground. Further, by inputting information about the surroundingbackground of the subject to the ANN model described in FIG. 9, theprocessor 180 may generate background information of the blank andcorrect a background in the blank based on the generated backgroundinformation of the blank.

FIG. 14 is a diagram illustrating an example of using a method ofcorrecting a background of a subject according to one embodiment of thepresent disclosure.

Referring to FIG. 14, when a user photographs a selfie using anintelligent device, a distortion phenomenon of a size of subjects mayoccur according to a distance.

For example, it may be assumed that the user is a first subject andfriends of the user are a second subject to a fourth subject. When aselfie of the first subject is photographed using an intelligent device,as shown in FIG. 14(a), the first subject closest to the camera may bephotographed largest, and the second subject to the fourth subjectrelatively farther from the camera may be photographed small.

When it is determined that the photographed first to fourth subjects arein a distorted state, the intelligent device may correct the distortedfirst to fourth subjects. The intelligent device may calculate a size ofthe detected first to fourth subjects and a distance between the cameraand the first to fourth subjects, respectively, and extract distortionstate information of the sizes of the first to fourth subjects accordingto the distance based on the size and the distance.

The intelligent device may correct a size of the first subject using theextracted distortion state information. As shown in FIG. 14(b), theintelligent device may adjust a size of the first subject to be smallerthan the size of the actually photographed first subject inconsideration of the size of the second subject to the size of thefourth subject.

When the size of the first subject is reduced and corrected, a blankcorresponding to the reduced size of the first subject may generate inthe photographed image.

As illustrated in FIG. 14(c), the intelligent device may correct a blankgenerated under the control of the processor. For example, if there is abackground in the blank, the intelligent device may correct a backgroundof the subject using a preview image. The intelligent device may store apreview image, which is an image before photographing a subject, andcorrect a blank using the stored preview image.

The intelligent device may obtain background information of the blankthrough the preview image, and correct a background in the blank usingthe obtained background information.

Alternatively, when there is no background in the blank, the intelligentdevice may correct the blank using a surrounding background of thesubject. For example, the intelligent device may combine the surroundingbackground of the subject and outlines of the subject to generate abackground of the blank. The intelligent device may input information onthe surrounding background of the subject to the ANN model described inFIG. 9 to generate background information of the blank and correct thebackground in the blank based on the generated background information ofthe blank.

FIGS. 15 and 16 are diagrams illustrating a method of measuring adistance between an intelligent device and a subject according to oneembodiment of the present disclosure.

Referring to FIGS. 15 and 16, the intelligent device may measure adistance of a subject using a built-in sensor and a light source. Thesensor may be a camera. The camera may be a time of flight (TOP) camera.TOP may be a method of shooting light and measuring a time of reflectedlight to calculate a distance. That is, the TOP camera may be a camerathat outputs a distance image using a TOP method.

The intelligent device may periodically turn on/off a light source toform a light pulse. The intelligent device may open/close an electronicshutter S₀ of the sensor at the same time point to input light pulses tothe sensor. The intelligent device may open/close a second electronicshutter S₁ at a time point in which the light source is turned off, andinput the reflected light pulse. An image input at S₀ may brightlyexpress a near subject and dark express a distant subject. That is, theimage input at S₀ may differently express brightness according to adistance of the subject due to a reflected light arrival time.

The image input at S₁ may darkly express a nearby subject and maybrightly express a distant subject as opposed to the image input at S₀.That is, because the shutter is not opened quickly in an early stage inthe image input at S₁, brightness may be expressed differently accordingto the distance of the subject.

As described above, the intelligent device may calculate an actualdistance from an intensity ratio of the light source using Equation 1.

$\begin{matrix}{d = {\frac{c}{2} \times t_{p} \times \frac{S_{1}}{S_{0} + S_{1}}}} & \left\lbrack {{Equation}\mspace{14mu} 1} \right\rbrack\end{matrix}$

d: distance, c: speed of light source, tp: light pulse duration, S₀:collected charge of initial shutter, S₁: collected charge of delayedshutter

The present disclosure described above may be implemented using acomputer-readable medium with programs recorded thereon for execution bya processor to perform various methods presented herein. Thecomputer-readable medium includes all kinds of recording devices capableof storing data that is readable by a computer system. Examples of thecomputer-readable mediums include hard disk drive (HDD), solid statedisk (SSD), silicon disk drive (SDD), ROM, RAM, CD-ROM, a magnetic tape,a floppy disk, an optical data storage device, the other types ofstorage mediums presented herein, and combinations thereof. If desired,the computer-readable medium may be realized in the form of a carrierwave (e.g., transmission over Internet). Thus, the foregoing descriptionis merely an example and is not to be considered as limiting the presentdisclosure. The scope of the present disclosure should be determined byrational interpretation of the appended claims, and all changes withinthe equivalent range of the present disclosure are included in the scopeof the present disclosure.

What is claimed is:
 1. A method of correcting an image of an intelligentdevice, the method comprising: obtaining subject state information froman image; determining a distortion state of a size of the subject basedon the obtained subject status information; measuring, when a distortionstate of the size of the subject is recognized, a distance between acamera and the subject and correcting the size of the subject to relatedto a ratio of the measured distance; and correcting a blank of a subjectbackground formed by the corrected size of the subject.
 2. The method ofclaim 1, wherein the correcting of a blank comprises obtainingbackground information about the subject through a preview imagephotographed before photographing.
 3. The method of claim 1, wherein thedetermining of a distortion state comprises: extracting feature valuesfrom the obtained subject state information; inputting the featurevalues to an artificial neural network (ANN) classifier trained todistinguish whether a size of the subject is in a distortion state;analyzing an output value of the ANN; and determining whether a size ofthe subject is in a distortion state based on the output value of theANN.
 4. The method of claim 3, further comprising: determining, when ablank occurs in the background by a corrected size of the subject,whether there is a background in the blank of the background;correcting, if there is a background in the blank of the background, thebackground of the subject base on the preview image; and correcting, ifthere is no background in the blank, the blank base on a surroundingbackground of the subject.
 5. The method of claim 3, further comprisingincreasing a correction range capable of correcting a size of thesubject as the measured distance is closer.
 6. The method of claim 5,wherein blank correction using a surrounding background of the subjectcomprises generating a background in the blank by combining thesurrounding background of the subject and outlines of the subject. 7.The method of claim 4, wherein the camera comprises a time of flight(TOP) camera for shooting light and measuring a time of reflected lightto calculate a distance.
 8. The method of claim 1, further comprisingreceiving, from a network, downlink control information (DCI) used forscheduling transmission of the subject state information obtained fromat least one sensor provided inside the intelligent device, wherein thesubject state information is transmitted to the network based on theDCI.
 9. The method of claim 4, further comprising performing an initialaccess procedure with the network based on a synchronization signalblock (SSB), wherein status information of the subject is transmitted tothe network through a physical uplink shared channel (PUSCH), whereinDM-RSs of the PUSCH and the SSB are QCL for QCL type D.
 10. The methodof claim 9, further comprising: controlling a transceiver to transmitstatus information of the subject to an AI processor included in thenetwork; and controlling the transceiver to receive AI processedinformation from the AI processor; and wherein the AI processedinformation is information in which a size of the subject is determinedto any one of a normal state or a distorted state.
 11. An intelligentdevice, comprising: a camera received in a body; a processor forcontrolling to obtain subject state information from an imagetransmitted from the camera; and a memory for storing the subject statusinformation, wherein the processor is configured to: determine adistortion state of a size of a subject based on the obtained subjectstate information, measure a distance between the camera and the subjectwhen the distortion state of the size of the subject is recognized,correct the size of the subject to related to a ratio of the measureddistance, and correct a blank of a subject background formed by thecorrected size of the subject.
 12. The intelligent device of claim 11,wherein the processor is configured to obtain background informationabout the subject through a preview image photographed beforephotographing.
 13. The intelligent device of claim 11, wherein theprocessor is configured to: extract feature values from the obtainedsubject state information, input the feature values to an artificialneural network (ANN) classifier trained to distinguish whether a size ofthe subject is in a distortion state, analyze an output value of theANN, and determine whether a size of the subject is in a distortionstate based on the output value of the ANN.
 14. The intelligent deviceof claim 13, wherein the processor is configured to: determine, when ablank occurs in the background by a corrected size of the subject,whether there is a background in the blank of the background, correct abackground of the subject base on the preview image, if there is abackground in the blank of the background, and correct the blank base ona surrounding background of the subject, if there is no background inthe blank.
 15. The intelligent device of claim 13, wherein the processoris configured to control to increase a correction range capable ofcorrecting a size of the subject as the measured distance is closer. 16.The intelligent device of claim 15, wherein blank correction base on asurrounding background of the subject comprises generating a backgroundin the blank by combining the surrounding background of the subject andoutlines of the subject.
 17. The intelligent device of claim 14, whereinthe camera comprises a time of flight (TOP) camera for shooting lightand measuring a time of reflected light to calculate a distance.
 18. Theintelligent device of claim 11, wherein the processor is configured toreceive, from a network, downlink control information (DCI) used forscheduling transmission of the subject state information obtained fromat least one sensor provided inside the intelligent device, and whereinthe subject state information is transmitted to the network based on theDCI.
 19. The intelligent device of claim 14, wherein the processor isconfigured to perform an initial access procedure with the network basedon a synchronization signal block (SSB), wherein status information ofthe subject is transmitted to the network through a physical uplinkshared channel (PUSCH), and wherein DM-RSs of the PUSCH and the SSB areQCL for QCL type D.
 20. The intelligent device of claim 19, furthercomprising a transceiver, wherein the processor is configured to:control to transmit state information of the subject to an AI processorincluded in the network through the transceiver, and control thetransceiver to receive AI processed information from the AI processor,and wherein the AI processed information is information in which a sizeof the subject is determined to any one of a normal state or a distortedstate.